Forecasting the information quality
Abstract
This paper considers information quality dimensions (Data Quality metrics). The approach and information technology to estimation and forecasting the data quality metric, which describes the accuracy of the information is proposed. A brief analysis of prior research on various approaches to defining metrics of Data Quality is presented. Nonlinearity of the data quality metric of accuracy makes it possible to forecast its future behavior using a stochastic volatility model (MSV), in which the Gibbs algorithm is used for parameter estimation. It is suggested to apply the information technology developed for forecasting the volatility of the exchange rate to forecast the future behavior of the uncertainty measure of the information accuracy. Forecasting of the accuracy measure of information has a key influence on the decision-making process.
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